The Performance of Regression-Based Variations of the Visual Scree for Determining the Number of Common Factors

Abstract

The accuracy and consistency of four regression-based variations of the visual scree (Cattell-Nelson-Gorsuch, multiple regression, t-value index, and standard error scree [SEscree]) were examined using artificial data with known characteristics. The results indicated that in general, the SEscree was more accurate than the other three procedures. The observation-to-variable ratio was most influential on the accuracy and the consistency of the number of factors yielded by the SEscree followed by the magnitude of factor structure and pattern coefficients. Unlike the other regression variations of the scree, the SEscree performed equally well whether the factors were correlated or uncorrelated.